Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Isolation and Flow Cytometric Analysis of Natural Killer Cells from Human Glioblastoma Multiforme (GBM) Tissues.

Current protocols·2025
Same author

A framework for using DNA methylation-based modelling for the clinical management of cranial meningioma.

Neuro-oncology·2025
Same author

The lateral orbitotomy approach: Technical nuances and video-illustration.

Clinical neurology and neurosurgery·2025
Same author

A New Area of Neurosurgery: First Robotic Neurosurgery Clinical Case Series.

Operative neurosurgery (Hagerstown, Md.)·2025
Same author

Assessment of molecular tools in pediatric, adolescent, and young adult meningioma highlights the need for lifespan precision in neuro-oncology.

Neuro-oncology·2025
Same author

Pituitary transposition techniques: surgical anatomy and technical nuances.

Journal of neurosurgery·2025

Related Experiment Video

Updated: Jan 11, 2026

Comprehensive Endovascular and Open Surgical Management of Cerebral Arteriovenous Malformations
14:58

Comprehensive Endovascular and Open Surgical Management of Cerebral Arteriovenous Malformations

Published on: October 20, 2017

10.2K

Artificial intelligence and machine learning driven segmentation and quantification models for brain arteriovenous

Mehmet Denizhan Yurtluk1, Kishore Balasubramanian2, Matthew P Blackwell3

  • 1Department of Neurological Surgery, Center for Image-Guided Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, United States.

Neuroradiology
|November 15, 2025
PubMed
Summary

Artificial intelligence (AI) and machine learning (ML) show promise for analyzing brain arteriovenous malformations (AVMs), aiding in segmentation and treatment planning. Further research is needed to improve model generalizability and clinical application.

Keywords:
Arteriovenous MalformationsArtificial IntelligenceDetectionMachine LearningSegmentation

More Related Videos

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.7K
A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage
08:12

A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage

Published on: July 28, 2018

8.4K

Related Experiment Videos

Last Updated: Jan 11, 2026

Comprehensive Endovascular and Open Surgical Management of Cerebral Arteriovenous Malformations
14:58

Comprehensive Endovascular and Open Surgical Management of Cerebral Arteriovenous Malformations

Published on: October 20, 2017

10.2K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.7K
A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage
08:12

A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage

Published on: July 28, 2018

8.4K

Area of Science:

  • Neuroimaging
  • Medical Artificial Intelligence

Background:

  • Brain arteriovenous malformations (AVMs) require accurate segmentation and treatment planning.
  • Current methods can be time-consuming and may lack standardization.

Purpose of the Study:

  • To systematically review the application of AI and ML for AVM segmentation, quantification, and treatment planning.
  • To evaluate the effectiveness of AI/ML models in AVM analysis.

Main Methods:

  • A PRISMA-guided systematic review was performed using major scientific databases.
  • Studies utilizing AI/ML for imaging-based AVM analysis were included.

Main Results:

  • Thirteen studies involving 3,010 individuals were analyzed.
  • Common modalities included TOF-MRA and MRI, with models like U-Net and SVM.
  • AI/ML demonstrated utility in risk assessment, segmentation, and stereotactic radiosurgery planning, achieving an average Dice score of 0.758.

Conclusions:

  • AI and ML hold potential for improving efficiency and standardization in AVM diagnosis and treatment.
  • Clinical implementation is limited by model generalizability; prospective validation and multimodal imaging integration are recommended.